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learning theory

518 papers

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generalization bound (649) sample complexity (1158) statistical learning (350) neural network (6616) online learning (1770) gradient descent (1143) stochastic gradient descent (1088) empirical risk minimization (348) convergence rate (606) pac learning (280)

Papers

Dimension-free Information Concentration via Exp-Concavity ALT 2018
Information Theoretic Guarantees for Empirical Risk Minimization with Applications to Model Selection and Large-Scale Optimization ICML 2018
Generalization Bounds for Uniformly Stable Algorithms NIPS 2018
Query Complexity of Bayesian Private Learning NIPS 2018
On how complexity affects the stability of a predictor AISTATS 2018
Time-Space Tradeoffs for Learning Finite Functions from Random Evaluations, with Applications to Polynomials COLT 2018
Distribution-Specific Hardness of Learning Neural Networks JMLR 2018
Nearly tight sample complexity bounds for learning mixtures of Gaussians via sample compression schemes NIPS 2018
Implicit Bias of Gradient Descent on Linear Convolutional Networks NIPS 2018
Conference on Learning Theory 2018: Preface COLT 2018
Learning to Branch ICML 2018
How Many Samples are Needed to Estimate a Convolutional Neural Network? NIPS 2018
The Limits of Post-Selection Generalization NIPS 2018
On Statistical Optimality of Variational Bayes AISTATS 2018
Approximation Guarantees for Adaptive Sampling ICML 2018
Optimal Cooperative Inference AISTATS 2018
Gradient descent with identity initialization efficiently learns positive definite linear transformations by deep residual networks ICML 2018
Chaining Mutual Information and Tightening Generalization Bounds NIPS 2018
Quantifying Learning Guarantees for Convex but Inconsistent Surrogates NIPS 2018
What Do Classifiers Actually Learn? a Case Study on Emotion Recognition Datasets INTERSPEECH 2018
A Property Testing Framework for the Theoretical Expressivity of Graph Kernels IJCAI 2018
Optimal Quantum Sample Complexity of Learning Algorithms JMLR 2018
On the Local Minima of the Empirical Risk NIPS 2018
Towards a Mathematical Understanding of the Difficulty in Learning With Feedforward Neural Networks CVPR 2018
Fast Rates of ERM and Stochastic Approximation: Adaptive to Error Bound Conditions NIPS 2018